Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations382
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory124.6 KiB
Average record size in memory334.1 B

Variable types

Categorical3
Text1
Numeric12

Alerts

10μ›” is highly overall correlated with 11μ›” and 9 other fieldsHigh correlation
11μ›” is highly overall correlated with 10μ›” and 9 other fieldsHigh correlation
12μ›” is highly overall correlated with 10μ›” and 9 other fieldsHigh correlation
1μ›” is highly overall correlated with 2μ›” and 4 other fieldsHigh correlation
2μ›” is highly overall correlated with 10μ›” and 10 other fieldsHigh correlation
3μ›” is highly overall correlated with 10μ›” and 10 other fieldsHigh correlation
4μ›” is highly overall correlated with 10μ›” and 10 other fieldsHigh correlation
5μ›” is highly overall correlated with 10μ›” and 10 other fieldsHigh correlation
6μ›” is highly overall correlated with 10μ›” and 10 other fieldsHigh correlation
7μ›” is highly overall correlated with 10μ›” and 9 other fieldsHigh correlation
8μ›” is highly overall correlated with 10μ›” and 9 other fieldsHigh correlation
9μ›” is highly overall correlated with 10μ›” and 9 other fieldsHigh correlation
1μ›” has 135 (35.3%) zeros Zeros
2μ›” has 230 (60.2%) zeros Zeros
3μ›” has 221 (57.9%) zeros Zeros
4μ›” has 227 (59.4%) zeros Zeros
5μ›” has 226 (59.2%) zeros Zeros
6μ›” has 224 (58.6%) zeros Zeros
7μ›” has 221 (57.9%) zeros Zeros
8μ›” has 218 (57.1%) zeros Zeros
9μ›” has 213 (55.8%) zeros Zeros
10μ›” has 214 (56.0%) zeros Zeros
11μ›” has 216 (56.5%) zeros Zeros
12μ›” has 213 (55.8%) zeros Zeros

Reproduction

Analysis started2025-03-25 07:33:16.153128
Analysis finished2025-03-25 07:33:35.918462
Duration19.77 seconds
Software versionydata-profiling vv4.16.0
Download configurationconfig.json

Variables

Distinct15
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size23.0 KiB
HMI
70 
Vietnam
62 
HMMA
53 
BHMC
50 
HMMI
40 
Other values (10)
107 

Length

Max length9
Median length4
Mean length4.3926702
Min length3

Characters and Unicode

Total characters1678
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st rowHMI
2nd rowHMI
3rd rowHMI
4th rowHMI
5th rowHMI

Common Values

ValueCountFrequency (%)
HMI 70
18.3%
Vietnam 62
16.2%
HMMA 53
13.9%
BHMC 50
13.1%
HMMI 40
10.5%
HAOS 24
 
6.3%
HMMC 24
 
6.3%
HMB 18
 
4.7%
HMMR 10
 
2.6%
Singapore 10
 
2.6%
Other values (5) 21
 
5.5%

Length

2025-03-25T16:33:35.996434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
hmi 70
18.3%
vietnam 62
16.2%
hmma 53
13.9%
bhmc 50
13.1%
hmmi 40
10.5%
haos 24
 
6.3%
hmmc 24
 
6.3%
hmb 18
 
4.7%
hmmr 10
 
2.6%
singapore 10
 
2.6%
Other values (5) 21
 
5.5%

Most occurring characters

ValueCountFrequency (%)
M 408
24.3%
H 299
17.8%
I 110
 
6.6%
A 81
 
4.8%
C 80
 
4.8%
i 74
 
4.4%
a 74
 
4.4%
B 74
 
4.4%
e 73
 
4.4%
n 72
 
4.3%
Other values (17) 333
19.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1678
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 408
24.3%
H 299
17.8%
I 110
 
6.6%
A 81
 
4.8%
C 80
 
4.8%
i 74
 
4.4%
a 74
 
4.4%
B 74
 
4.4%
e 73
 
4.4%
n 72
 
4.3%
Other values (17) 333
19.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1678
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 408
24.3%
H 299
17.8%
I 110
 
6.6%
A 81
 
4.8%
C 80
 
4.8%
i 74
 
4.4%
a 74
 
4.4%
B 74
 
4.4%
e 73
 
4.4%
n 72
 
4.3%
Other values (17) 333
19.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1678
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 408
24.3%
H 299
17.8%
I 110
 
6.6%
A 81
 
4.8%
C 80
 
4.8%
i 74
 
4.4%
a 74
 
4.4%
B 74
 
4.4%
e 73
 
4.4%
n 72
 
4.3%
Other values (17) 333
19.8%
Distinct115
Distinct (%)30.1%
Missing0
Missing (%)0.0%
Memory size26.4 KiB
2025-03-25T16:33:36.519218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length24
Median length19
Mean length13.348168
Min length2

Characters and Unicode

Total characters5099
Distinct characters61
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)5.0%

Sample

1st row Santro (AH2)
2nd row Xcent (BA 4DR)
3rd row Aura (AI3 4DR)
4th row NIOS (AI3 5DR)
5th row i20 (BI3 5DR)
ValueCountFrequency (%)
ev 43
 
4.8%
tucson 39
 
4.3%
creta 32
 
3.5%
hev 32
 
3.5%
santa-fe 31
 
3.4%
ioniq5 27
 
3.0%
n 19
 
2.1%
bc3 18
 
2.0%
i20 18
 
2.0%
5dr 16
 
1.8%
Other values (133) 629
69.6%
2025-03-25T16:33:37.106596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
663
 
13.0%
( 363
 
7.1%
) 363
 
7.1%
a 329
 
6.5%
e 182
 
3.6%
n 176
 
3.5%
t 172
 
3.4%
i 151
 
3.0%
N 150
 
2.9%
S 142
 
2.8%
Other values (51) 2408
47.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5099
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
663
 
13.0%
( 363
 
7.1%
) 363
 
7.1%
a 329
 
6.5%
e 182
 
3.6%
n 176
 
3.5%
t 172
 
3.4%
i 151
 
3.0%
N 150
 
2.9%
S 142
 
2.8%
Other values (51) 2408
47.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5099
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
663
 
13.0%
( 363
 
7.1%
) 363
 
7.1%
a 329
 
6.5%
e 182
 
3.6%
n 176
 
3.5%
t 172
 
3.4%
i 151
 
3.0%
N 150
 
2.9%
S 142
 
2.8%
Other values (51) 2408
47.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5099
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
663
 
13.0%
( 363
 
7.1%
) 363
 
7.1%
a 329
 
6.5%
e 182
 
3.6%
n 176
 
3.5%
t 172
 
3.4%
i 151
 
3.0%
N 150
 
2.9%
S 142
 
2.8%
Other values (51) 2408
47.2%
Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size36.7 KiB
λ‚΄μˆ˜μš©
230 
수좜용
152 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1146
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowλ‚΄μˆ˜μš©
2nd rowλ‚΄μˆ˜μš©
3rd rowλ‚΄μˆ˜μš©
4th rowλ‚΄μˆ˜μš©
5th rowλ‚΄μˆ˜μš©

Common Values

ValueCountFrequency (%)
λ‚΄μˆ˜μš© 230
60.2%
수좜용 152
39.8%

Length

2025-03-25T16:33:37.205539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T16:33:37.270516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
λ‚΄μˆ˜μš© 230
60.2%
수좜용 152
39.8%

Most occurring characters

ValueCountFrequency (%)
수 382
33.3%
용 382
33.3%
λ‚΄ 230
20.1%
좜 152
 
13.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1146
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
수 382
33.3%
용 382
33.3%
λ‚΄ 230
20.1%
좜 152
 
13.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1146
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
수 382
33.3%
용 382
33.3%
λ‚΄ 230
20.1%
좜 152
 
13.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1146
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
수 382
33.3%
용 382
33.3%
λ‚΄ 230
20.1%
좜 152
 
13.3%

1μ›”
Real number (ℝ)

High correlation  Zeros 

Distinct222
Distinct (%)58.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1386.911
Minimum0
Maximum16787
Zeros135
Zeros (%)35.3%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-03-25T16:33:37.450398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median98.5
Q31470.75
95-th percentile7079.3
Maximum16787
Range16787
Interquartile range (IQR)1470.75

Descriptive statistics

Standard deviation2727.0732
Coefficient of variation (CV)1.9662929
Kurtosis8.9588144
Mean1386.911
Median Absolute Deviation (MAD)98.5
Skewness2.8319636
Sum529800
Variance7436928.3
MonotonicityNot monotonic
2025-03-25T16:33:37.590328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 135
35.3%
1 6
 
1.6%
60 5
 
1.3%
30 4
 
1.0%
120 3
 
0.8%
80 3
 
0.8%
157 2
 
0.5%
57 2
 
0.5%
10 2
 
0.5%
2000 2
 
0.5%
Other values (212) 218
57.1%
ValueCountFrequency (%)
0 135
35.3%
1 6
 
1.6%
2 1
 
0.3%
4 1
 
0.3%
8 1
 
0.3%
9 1
 
0.3%
10 2
 
0.5%
16 1
 
0.3%
20 1
 
0.3%
21 1
 
0.3%
ValueCountFrequency (%)
16787 1
0.3%
15840 1
0.3%
15037 1
0.3%
13212 1
0.3%
12572 1
0.3%
11831 1
0.3%
11607 1
0.3%
11106 1
0.3%
10738 1
0.3%
10069 1
0.3%

2μ›”
Real number (ℝ)

High correlation  Zeros 

Distinct142
Distinct (%)37.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean919.37173
Minimum0
Maximum15276
Zeros230
Zeros (%)60.2%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-03-25T16:33:37.725242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3380.75
95-th percentile6454.15
Maximum15276
Range15276
Interquartile range (IQR)380.75

Descriptive statistics

Standard deviation2329.8467
Coefficient of variation (CV)2.5341727
Kurtosis12.150356
Mean919.37173
Median Absolute Deviation (MAD)0
Skewness3.3773444
Sum351200
Variance5428185.6
MonotonicityNot monotonic
2025-03-25T16:33:37.869159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 230
60.2%
90 4
 
1.0%
30 3
 
0.8%
51 2
 
0.5%
1 2
 
0.5%
877 2
 
0.5%
19 2
 
0.5%
181 2
 
0.5%
24 2
 
0.5%
3480 1
 
0.3%
Other values (132) 132
34.6%
ValueCountFrequency (%)
0 230
60.2%
1 2
 
0.5%
4 1
 
0.3%
10 1
 
0.3%
11 1
 
0.3%
12 1
 
0.3%
19 2
 
0.5%
20 1
 
0.3%
23 1
 
0.3%
24 2
 
0.5%
ValueCountFrequency (%)
15276 1
0.3%
12699 1
0.3%
12684 1
0.3%
12653 1
0.3%
11553 1
0.3%
10421 1
0.3%
9997 1
0.3%
9749 1
0.3%
9635 1
0.3%
9287 1
0.3%

3μ›”
Real number (ℝ)

High correlation  Zeros 

Distinct157
Distinct (%)41.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1061.9162
Minimum0
Maximum16458
Zeros221
Zeros (%)57.9%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-03-25T16:33:38.173985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3449.5
95-th percentile6673.9
Maximum16458
Range16458
Interquartile range (IQR)449.5

Descriptive statistics

Standard deviation2667.7584
Coefficient of variation (CV)2.5122117
Kurtosis11.667484
Mean1061.9162
Median Absolute Deviation (MAD)0
Skewness3.346154
Sum405652
Variance7116934.8
MonotonicityNot monotonic
2025-03-25T16:33:38.317913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 221
57.9%
30 2
 
0.5%
71 2
 
0.5%
1 2
 
0.5%
40 2
 
0.5%
297 2
 
0.5%
2786 1
 
0.3%
12313 1
 
0.3%
437 1
 
0.3%
59 1
 
0.3%
Other values (147) 147
38.5%
ValueCountFrequency (%)
0 221
57.9%
1 2
 
0.5%
2 1
 
0.3%
3 1
 
0.3%
4 1
 
0.3%
5 1
 
0.3%
6 1
 
0.3%
8 1
 
0.3%
9 1
 
0.3%
12 1
 
0.3%
ValueCountFrequency (%)
16458 1
0.3%
15337 1
0.3%
14305 1
0.3%
14026 1
0.3%
12425 1
0.3%
12313 1
0.3%
12105 1
0.3%
11326 1
0.3%
10697 1
0.3%
10559 1
0.3%

4μ›”
Real number (ℝ)

High correlation  Zeros 

Distinct149
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean928.20419
Minimum0
Maximum15447
Zeros227
Zeros (%)59.4%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-03-25T16:33:38.456823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3457.25
95-th percentile5590.85
Maximum15447
Range15447
Interquartile range (IQR)457.25

Descriptive statistics

Standard deviation2327.0925
Coefficient of variation (CV)2.5070911
Kurtosis13.055139
Mean928.20419
Median Absolute Deviation (MAD)0
Skewness3.4527402
Sum354574
Variance5415359.5
MonotonicityNot monotonic
2025-03-25T16:33:38.602740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 227
59.4%
30 3
 
0.8%
2 3
 
0.8%
28 2
 
0.5%
3 2
 
0.5%
240 2
 
0.5%
3407 1
 
0.3%
2644 1
 
0.3%
482 1
 
0.3%
624 1
 
0.3%
Other values (139) 139
36.4%
ValueCountFrequency (%)
0 227
59.4%
1 1
 
0.3%
2 3
 
0.8%
3 2
 
0.5%
6 1
 
0.3%
7 1
 
0.3%
13 1
 
0.3%
21 1
 
0.3%
27 1
 
0.3%
28 2
 
0.5%
ValueCountFrequency (%)
15447 1
0.3%
14186 1
0.3%
12803 1
0.3%
11864 1
0.3%
11029 1
0.3%
11016 1
0.3%
10577 1
0.3%
10386 1
0.3%
10342 1
0.3%
9120 1
0.3%

5μ›”
Real number (ℝ)

High correlation  Zeros 

Distinct150
Distinct (%)39.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean988.12042
Minimum0
Maximum14662
Zeros226
Zeros (%)59.2%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-03-25T16:33:38.744668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3417.5
95-th percentile6370.45
Maximum14662
Range14662
Interquartile range (IQR)417.5

Descriptive statistics

Standard deviation2449.293
Coefficient of variation (CV)2.4787394
Kurtosis12.330392
Mean988.12042
Median Absolute Deviation (MAD)0
Skewness3.363813
Sum377462
Variance5999036.4
MonotonicityNot monotonic
2025-03-25T16:33:38.885587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 226
59.2%
1 3
 
0.8%
410 2
 
0.5%
90 2
 
0.5%
2 2
 
0.5%
150 2
 
0.5%
60 2
 
0.5%
3427 1
 
0.3%
6689 1
 
0.3%
3617 1
 
0.3%
Other values (140) 140
36.6%
ValueCountFrequency (%)
0 226
59.2%
1 3
 
0.8%
2 2
 
0.5%
3 1
 
0.3%
4 1
 
0.3%
5 1
 
0.3%
7 1
 
0.3%
11 1
 
0.3%
12 1
 
0.3%
15 1
 
0.3%
ValueCountFrequency (%)
14662 1
0.3%
14449 1
0.3%
14152 1
0.3%
13972 1
0.3%
13623 1
0.3%
10483 1
0.3%
10474 1
0.3%
10213 1
0.3%
9612 1
0.3%
9327 1
0.3%

6μ›”
Real number (ℝ)

High correlation  Zeros 

Distinct153
Distinct (%)40.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1014.4607
Minimum0
Maximum16293
Zeros224
Zeros (%)58.6%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-03-25T16:33:39.024499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3423.5
95-th percentile6158.85
Maximum16293
Range16293
Interquartile range (IQR)423.5

Descriptive statistics

Standard deviation2544.3795
Coefficient of variation (CV)2.5081104
Kurtosis12.840479
Mean1014.4607
Median Absolute Deviation (MAD)0
Skewness3.4505726
Sum387524
Variance6473867
MonotonicityNot monotonic
2025-03-25T16:33:39.169430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 224
58.6%
30 2
 
0.5%
193 2
 
0.5%
1 2
 
0.5%
3 2
 
0.5%
7 2
 
0.5%
60 2
 
0.5%
12834 1
 
0.3%
3113 1
 
0.3%
2193 1
 
0.3%
Other values (143) 143
37.4%
ValueCountFrequency (%)
0 224
58.6%
1 2
 
0.5%
2 1
 
0.3%
3 2
 
0.5%
5 1
 
0.3%
7 2
 
0.5%
15 1
 
0.3%
22 1
 
0.3%
26 1
 
0.3%
29 1
 
0.3%
ValueCountFrequency (%)
16293 1
0.3%
14447 1
0.3%
14160 1
0.3%
13613 1
0.3%
12834 1
0.3%
12618 1
0.3%
11871 1
0.3%
11606 1
0.3%
10598 1
0.3%
10459 1
0.3%

7μ›”
Real number (ℝ)

High correlation  Zeros 

Distinct154
Distinct (%)40.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean920.53665
Minimum0
Maximum17350
Zeros221
Zeros (%)57.9%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-03-25T16:33:39.325327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3433.25
95-th percentile5372.15
Maximum17350
Range17350
Interquartile range (IQR)433.25

Descriptive statistics

Standard deviation2261.5127
Coefficient of variation (CV)2.456733
Kurtosis14.643004
Mean920.53665
Median Absolute Deviation (MAD)0
Skewness3.5060292
Sum351645
Variance5114439.8
MonotonicityNot monotonic
2025-03-25T16:33:39.478248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 221
57.9%
60 2
 
0.5%
5 2
 
0.5%
13 2
 
0.5%
1 2
 
0.5%
145 2
 
0.5%
76 2
 
0.5%
32 2
 
0.5%
62 2
 
0.5%
4564 1
 
0.3%
Other values (144) 144
37.7%
ValueCountFrequency (%)
0 221
57.9%
1 2
 
0.5%
3 1
 
0.3%
5 2
 
0.5%
6 1
 
0.3%
7 1
 
0.3%
8 1
 
0.3%
13 2
 
0.5%
20 1
 
0.3%
32 2
 
0.5%
ValueCountFrequency (%)
17350 1
0.3%
14062 1
0.3%
12304 1
0.3%
10270 1
0.3%
10062 1
0.3%
9855 1
0.3%
9827 1
0.3%
9268 1
0.3%
8840 1
0.3%
7944 1
0.3%

8μ›”
Real number (ℝ)

High correlation  Zeros 

Distinct156
Distinct (%)40.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean998.06545
Minimum0
Maximum16762
Zeros218
Zeros (%)57.1%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-03-25T16:33:39.617169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3445.5
95-th percentile5384.95
Maximum16762
Range16762
Interquartile range (IQR)445.5

Descriptive statistics

Standard deviation2508.5711
Coefficient of variation (CV)2.5134335
Kurtosis14.346765
Mean998.06545
Median Absolute Deviation (MAD)0
Skewness3.59781
Sum381261
Variance6292929
MonotonicityNot monotonic
2025-03-25T16:33:39.763076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 218
57.1%
40 4
 
1.0%
1 3
 
0.8%
90 2
 
0.5%
28 2
 
0.5%
50 2
 
0.5%
6 2
 
0.5%
500 1
 
0.3%
1104 1
 
0.3%
160 1
 
0.3%
Other values (146) 146
38.2%
ValueCountFrequency (%)
0 218
57.1%
1 3
 
0.8%
2 1
 
0.3%
6 2
 
0.5%
10 1
 
0.3%
20 1
 
0.3%
28 2
 
0.5%
30 1
 
0.3%
33 1
 
0.3%
40 4
 
1.0%
ValueCountFrequency (%)
16762 1
0.3%
15138 1
0.3%
14515 1
0.3%
13832 1
0.3%
12993 1
0.3%
11975 1
0.3%
11842 1
0.3%
10948 1
0.3%
10858 1
0.3%
9823 1
0.3%

9μ›”
Real number (ℝ)

High correlation  Zeros 

Distinct152
Distinct (%)39.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1052.7461
Minimum0
Maximum15902
Zeros213
Zeros (%)55.8%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-03-25T16:33:39.913000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3460
95-th percentile6389.35
Maximum15902
Range15902
Interquartile range (IQR)460

Descriptive statistics

Standard deviation2560.1908
Coefficient of variation (CV)2.4319167
Kurtosis11.128201
Mean1052.7461
Median Absolute Deviation (MAD)0
Skewness3.249226
Sum402149
Variance6554576.9
MonotonicityNot monotonic
2025-03-25T16:33:40.059920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 213
55.8%
1 4
 
1.0%
3 4
 
1.0%
60 3
 
0.8%
675 2
 
0.5%
4428 2
 
0.5%
101 2
 
0.5%
360 2
 
0.5%
13 2
 
0.5%
106 2
 
0.5%
Other values (142) 146
38.2%
ValueCountFrequency (%)
0 213
55.8%
1 4
 
1.0%
2 1
 
0.3%
3 4
 
1.0%
9 1
 
0.3%
11 1
 
0.3%
12 1
 
0.3%
13 2
 
0.5%
18 1
 
0.3%
22 1
 
0.3%
ValueCountFrequency (%)
15902 1
0.3%
13343 1
0.3%
13221 1
0.3%
12878 1
0.3%
12717 1
0.3%
12370 1
0.3%
12204 1
0.3%
12198 1
0.3%
11447 1
0.3%
10566 1
0.3%

10μ›”
Real number (ℝ)

High correlation  Zeros 

Distinct159
Distinct (%)41.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1079.0471
Minimum0
Maximum17497
Zeros214
Zeros (%)56.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-03-25T16:33:40.370728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3518
95-th percentile7042.2
Maximum17497
Range17497
Interquartile range (IQR)518

Descriptive statistics

Standard deviation2688.485
Coefficient of variation (CV)2.4915362
Kurtosis12.378516
Mean1079.0471
Median Absolute Deviation (MAD)0
Skewness3.3982025
Sum412196
Variance7227951.7
MonotonicityNot monotonic
2025-03-25T16:33:40.515646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 214
56.0%
20 3
 
0.8%
2 3
 
0.8%
44 2
 
0.5%
60 2
 
0.5%
21 2
 
0.5%
756 2
 
0.5%
177 2
 
0.5%
24 2
 
0.5%
2406 1
 
0.3%
Other values (149) 149
39.0%
ValueCountFrequency (%)
0 214
56.0%
1 1
 
0.3%
2 3
 
0.8%
3 1
 
0.3%
5 1
 
0.3%
7 1
 
0.3%
8 1
 
0.3%
13 1
 
0.3%
17 1
 
0.3%
20 3
 
0.8%
ValueCountFrequency (%)
17497 1
0.3%
15261 1
0.3%
14717 1
0.3%
14087 1
0.3%
13335 1
0.3%
13077 1
0.3%
11993 1
0.3%
11581 1
0.3%
10991 1
0.3%
10901 1
0.3%

11μ›”
Real number (ℝ)

High correlation  Zeros 

Distinct154
Distinct (%)40.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1040.144
Minimum0
Maximum15452
Zeros216
Zeros (%)56.5%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-03-25T16:33:40.652576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3477.25
95-th percentile6967.4
Maximum15452
Range15452
Interquartile range (IQR)477.25

Descriptive statistics

Standard deviation2547.9012
Coefficient of variation (CV)2.4495658
Kurtosis10.755677
Mean1040.144
Median Absolute Deviation (MAD)0
Skewness3.2237643
Sum397335
Variance6491800.3
MonotonicityNot monotonic
2025-03-25T16:33:40.793487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 216
56.5%
10 4
 
1.0%
62 3
 
0.8%
1 3
 
0.8%
3 3
 
0.8%
172 2
 
0.5%
800 2
 
0.5%
16 2
 
0.5%
120 2
 
0.5%
66 1
 
0.3%
Other values (144) 144
37.7%
ValueCountFrequency (%)
0 216
56.5%
1 3
 
0.8%
2 1
 
0.3%
3 3
 
0.8%
4 1
 
0.3%
6 1
 
0.3%
7 1
 
0.3%
9 1
 
0.3%
10 4
 
1.0%
12 1
 
0.3%
ValueCountFrequency (%)
15452 1
0.3%
14248 1
0.3%
12969 1
0.3%
12167 1
0.3%
12071 1
0.3%
11814 1
0.3%
11775 1
0.3%
11655 1
0.3%
11206 1
0.3%
11180 1
0.3%

12μ›”
Real number (ℝ)

High correlation  Zeros 

Distinct157
Distinct (%)41.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean948.15969
Minimum0
Maximum14579
Zeros213
Zeros (%)55.8%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-03-25T16:33:40.940403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3446.25
95-th percentile5874.85
Maximum14579
Range14579
Interquartile range (IQR)446.25

Descriptive statistics

Standard deviation2331.8766
Coefficient of variation (CV)2.4593712
Kurtosis10.748374
Mean948.15969
Median Absolute Deviation (MAD)0
Skewness3.2270306
Sum362197
Variance5437648.6
MonotonicityNot monotonic
2025-03-25T16:33:41.087333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 213
55.8%
1 5
 
1.3%
3 3
 
0.8%
19 3
 
0.8%
60 3
 
0.8%
33 2
 
0.5%
179 2
 
0.5%
72 2
 
0.5%
1693 1
 
0.3%
1758 1
 
0.3%
Other values (147) 147
38.5%
ValueCountFrequency (%)
0 213
55.8%
1 5
 
1.3%
2 1
 
0.3%
3 3
 
0.8%
6 1
 
0.3%
7 1
 
0.3%
9 1
 
0.3%
10 1
 
0.3%
11 1
 
0.3%
12 1
 
0.3%
ValueCountFrequency (%)
14579 1
0.3%
12608 1
0.3%
11598 1
0.3%
11547 1
0.3%
11472 1
0.3%
10415 1
0.3%
10383 1
0.3%
10265 1
0.3%
10065 1
0.3%
9420 1
0.3%

연도
Categorical

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size22.9 KiB
2025
132 
2024
130 
2023
120 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1528
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023
2nd row2023
3rd row2023
4th row2023
5th row2023

Common Values

ValueCountFrequency (%)
2025 132
34.6%
2024 130
34.0%
2023 120
31.4%

Length

2025-03-25T16:33:41.205251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T16:33:41.274215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2025 132
34.6%
2024 130
34.0%
2023 120
31.4%

Most occurring characters

ValueCountFrequency (%)
2 764
50.0%
0 382
25.0%
5 132
 
8.6%
4 130
 
8.5%
3 120
 
7.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1528
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 764
50.0%
0 382
25.0%
5 132
 
8.6%
4 130
 
8.5%
3 120
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1528
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 764
50.0%
0 382
25.0%
5 132
 
8.6%
4 130
 
8.5%
3 120
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1528
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 764
50.0%
0 382
25.0%
5 132
 
8.6%
4 130
 
8.5%
3 120
 
7.9%

Interactions

2025-03-25T16:33:33.922198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:16.710845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:18.140909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:19.622958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:21.197699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:22.713138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:24.175302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:25.937308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:27.415544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:29.109669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:30.649045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:32.155178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:34.052531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:16.835926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:18.265838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:19.741104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:21.325613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:22.842064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:24.307226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:26.059239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:27.530478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:29.240595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:30.770974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:32.299087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:34.175475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:16.963032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:18.392662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:19.863508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:21.449541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:22.959998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:24.520105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:26.186152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:27.661404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:29.373708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:30.895900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:32.423525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:34.318394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:17.075224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:18.511594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:19.977442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:21.573470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:23.075931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:24.642035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:26.299087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:27.791330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:29.503633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:31.020818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:32.550982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:34.444316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:17.197139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:18.638526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:20.097374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:21.703396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:23.197875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:24.924884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:26.426028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:27.924253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:29.631560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:31.146745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:32.685895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:34.577231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:17.311088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:18.754455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:20.220304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:21.828324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:23.314794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:25.046813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:26.549953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:28.052180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:29.755489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:31.264678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:32.814821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:34.701174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:17.436369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:18.878374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:20.341068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:21.952268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:23.438737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:25.173745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:26.674944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:28.185104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:29.882431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:31.393618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:32.964735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:34.824094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:17.549984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:18.997307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:20.456015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:22.078181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:23.567649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:25.303671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:26.799860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:28.315030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:30.009345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:31.512550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:33.086680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:34.946036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:17.662457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:19.124241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:20.733842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:22.206124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:23.684597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:25.428585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:26.916794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:28.443966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:30.138271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:31.651471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:33.388503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:35.075961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:17.785397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:19.249163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:20.842904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:22.335354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:23.809511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:25.557514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:27.038724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:28.573882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:30.263215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:31.780383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:33.522431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:35.287824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:17.898544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:19.377090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:20.958822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:22.460283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:23.930456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:25.685438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:27.164688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:28.698810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:30.401172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:31.902323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:33.658339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:35.419749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:18.016980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:19.508015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:21.081761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:22.590218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:24.054385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:25.811376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:27.287617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:28.831828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:30.525115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:32.029256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-25T16:33:33.793262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-25T16:33:41.359177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
10μ›”11μ›”12μ›”1μ›”2μ›”3μ›”4μ›”5μ›”6μ›”7μ›”8μ›”9월곡μž₯λͺ…(κ΅­κ°€)μ—°λ„νŒλ§€ ꡬ뢄
10μ›”1.0000.9760.9560.4270.7940.8230.8290.8730.8870.9340.9440.9690.0000.2030.000
11μ›”0.9761.0000.9610.4130.7810.8100.8200.8620.8740.9280.9370.9720.0000.2210.073
12μ›”0.9560.9611.0000.4180.7900.8050.8120.8490.8660.9140.9230.9460.0000.1760.000
1μ›”0.4270.4130.4181.0000.6230.5880.5710.5470.5280.4890.4460.4340.0440.1000.000
2μ›”0.7940.7810.7900.6231.0000.9530.9290.9070.8870.8550.8220.7990.0000.2190.084
3μ›”0.8230.8100.8050.5880.9531.0000.9720.9330.9190.8780.8420.8430.0000.1940.028
4μ›”0.8290.8200.8120.5710.9290.9721.0000.9480.9240.8820.8540.8540.0000.1880.000
5μ›”0.8730.8620.8490.5470.9070.9330.9481.0000.9660.9220.8930.8840.0000.1920.000
6μ›”0.8870.8740.8660.5280.8870.9190.9240.9661.0000.9430.9120.9030.0000.2030.000
7μ›”0.9340.9280.9140.4890.8550.8780.8820.9220.9431.0000.9490.9380.0370.1860.036
8μ›”0.9440.9370.9230.4460.8220.8420.8540.8930.9120.9491.0000.9500.0000.1830.086
9μ›”0.9690.9720.9460.4340.7990.8430.8540.8840.9030.9380.9501.0000.0000.2160.056
곡μž₯λͺ…(κ΅­κ°€)0.0000.0000.0000.0440.0000.0000.0000.0000.0000.0370.0000.0001.0000.0000.436
연도0.2030.2210.1760.1000.2190.1940.1880.1920.2030.1860.1830.2160.0001.0000.000
판맀 ꡬ뢄0.0000.0730.0000.0000.0840.0280.0000.0000.0000.0360.0860.0560.4360.0001.000

Missing values

2025-03-25T16:33:35.633626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-25T16:33:35.812524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

곡μž₯λͺ…(κ΅­κ°€)μ°¨λŸ‰ λͺ¨λΈνŒλ§€ ꡬ뢄1μ›”2μ›”3μ›”4μ›”5μ›”6μ›”7μ›”8μ›”9μ›”10μ›”11μ›”12월연도
0HMISantro (AH2)λ‚΄μˆ˜μš©0000000000002023
1HMIXcent (BA 4DR)λ‚΄μˆ˜μš©0000000000002023
2HMIAura (AI3 4DR)λ‚΄μˆ˜μš©4634552437745085470749074514489239004096385038122023
3HMINIOS (AI3 5DR)λ‚΄μˆ˜μš©8760963593046839638563215337730652236552470852472023
4HMIi20 (BI3 5DR)λ‚΄μˆ˜μš©8185928765966472609461625001489664817212572745742023
5HMIVerna (Hci)λ‚΄μˆ˜μš©9954700000000002023
6HMIVerna (BN7i)λ‚΄μˆ˜μš©003755400136874001285825762610231317017122023
7HMIExter (AI3 SUV)λ‚΄μˆ˜μš©0000007000743086478097832575142023
8HMIVenue (QXi)λ‚΄μˆ˜μš©107389997100241034210213116061006210948122041158111180103832023
9HMICreta (SU2i)λ‚΄μˆ˜μš©150371042114026141861444914447140621383212717130771181492432023
곡μž₯λͺ…(κ΅­κ°€)μ°¨λŸ‰ λͺ¨λΈνŒλ§€ ꡬ뢄1μ›”2μ›”3μ›”4μ›”5μ›”6μ›”7μ›”8μ›”9μ›”10μ›”11μ›”12월연도
372VietnamCounty (CSv)λ‚΄μˆ˜μš©66000000000002025
373VietnamSolati (Euv)λ‚΄μˆ˜μš©257000000000002025
374VietnamSanta Fe (MX5v)λ‚΄μˆ˜μš©731000000000002025
375VietnamSanta Fe (MX5v)수좜용1000000000002025
376VietnamPalisade (LX2v)수좜용78000000000002025
377VietnamSolati (Euv)수좜용24000000000002025
378SingaporeIONIQ5 (NE)λ‚΄μˆ˜μš©4000000000002025
379SingaporeIONIQ6 (CE)λ‚΄μˆ˜μš©9000000000002025
380SingaporeIONIQ5 (NE)수좜용0000000000002025
381SingaporeIONIQ5 Robotaxi (NE R)수좜용0000000000002025